


'''simple docstring'''


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class        lowercase__    (		_snake_case   ):





							'''simple docstring'''


							A_				: Optional[Any]       =    (EulerDiscreteScheduler,)
							A_				: List[str]       =    10






							def UpperCAmelCase_      (       self       ,   **__snake_case    ):
											_SCREAMING_SNAKE_CASE					:			str							= {
											    """num_train_timesteps""": 1100,
											    """beta_start""": 0.0001,
											    """beta_end""": 0.02,
											    """beta_schedule""": """linear""",
											}

											config.update(**__snake_case    )
											return config






							def UpperCAmelCase_      (       self    ):
											for timesteps in [10, 50, 100, 1000]:
															self.check_over_configs(num_train_timesteps=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]       ,   [0.0002, 0.002, 0.02]    ):
															self.check_over_configs(beta_start=__snake_case       ,   beta_end=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											for schedule in ["linear", "scaled_linear"]:
															self.check_over_configs(beta_schedule=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(prediction_type=__snake_case    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Tuple							= self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE					:			int							= self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE					:			List[Any]							= scheduler_class(**__snake_case    )

											scheduler.set_timesteps(self.num_inference_steps    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.dummy_model()
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= self.dummy_sample_deter * scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE					:			List[str]							= sample.to(__snake_case    )

											for i, t in enumerate(scheduler.timesteps    ):
															_SCREAMING_SNAKE_CASE					:			Tuple							= scheduler.scale_model_input(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			int							= model(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= scheduler.step(__snake_case       ,   __snake_case       ,   __snake_case       ,   generator=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			str							= output.prev_sample

											_SCREAMING_SNAKE_CASE					:			Any							= torch.sum(torch.abs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			str							= torch.mean(torch.abs(__snake_case    )    )

											assert abs(result_sum.item() - 10.0807    ) < 1e-2
											assert abs(result_mean.item() - 0.0131    ) < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.get_scheduler_config(prediction_type="""v_prediction"""    )
											_SCREAMING_SNAKE_CASE					:			int							= scheduler_class(**__snake_case    )

											scheduler.set_timesteps(self.num_inference_steps    )

											_SCREAMING_SNAKE_CASE					:			int							= torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			List[str]							= self.dummy_model()
											_SCREAMING_SNAKE_CASE					:			Any							= self.dummy_sample_deter * scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE					:			int							= sample.to(__snake_case    )

											for i, t in enumerate(scheduler.timesteps    ):
															_SCREAMING_SNAKE_CASE					:			Tuple							= scheduler.scale_model_input(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= model(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			Tuple							= scheduler.step(__snake_case       ,   __snake_case       ,   __snake_case       ,   generator=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= output.prev_sample

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= torch.sum(torch.abs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			int							= torch.mean(torch.abs(__snake_case    )    )

											assert abs(result_sum.item() - 0.0002    ) < 1e-2
											assert abs(result_mean.item() - 2.2676e-06    ) < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE					:			str							= self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= scheduler_class(**__snake_case    )

											scheduler.set_timesteps(self.num_inference_steps       ,   device=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			str							= self.dummy_model()
											_SCREAMING_SNAKE_CASE					:			Dict							= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= sample.to(__snake_case    )

											for t in scheduler.timesteps:
															_SCREAMING_SNAKE_CASE					:			List[str]							= scheduler.scale_model_input(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			Dict							= model(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			List[str]							= scheduler.step(__snake_case       ,   __snake_case       ,   __snake_case       ,   generator=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= output.prev_sample

											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.sum(torch.abs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= torch.mean(torch.abs(__snake_case    )    )

											assert abs(result_sum.item() - 10.0807    ) < 1e-2
											assert abs(result_mean.item() - 0.0131    ) < 1e-3






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.scheduler_classes[0]
											_SCREAMING_SNAKE_CASE					:			List[str]							= self.get_scheduler_config()
											_SCREAMING_SNAKE_CASE					:			int							= scheduler_class(**__snake_case       ,   use_karras_sigmas=__snake_case    )

											scheduler.set_timesteps(self.num_inference_steps       ,   device=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= torch.manual_seed(0    )

											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= self.dummy_model()
											_SCREAMING_SNAKE_CASE					:			int							= self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											_SCREAMING_SNAKE_CASE					:			Any							= sample.to(__snake_case    )

											for t in scheduler.timesteps:
															_SCREAMING_SNAKE_CASE					:			int							= scheduler.scale_model_input(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= model(__snake_case       ,   __snake_case    )

															_SCREAMING_SNAKE_CASE					:			List[str]							= scheduler.step(__snake_case       ,   __snake_case       ,   __snake_case       ,   generator=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= output.prev_sample

											_SCREAMING_SNAKE_CASE					:			str							= torch.sum(torch.abs(__snake_case    )    )
											_SCREAMING_SNAKE_CASE					:			str							= torch.mean(torch.abs(__snake_case    )    )

											assert abs(result_sum.item() - 124.52299499511719    ) < 1e-2
											assert abs(result_mean.item() - 0.16213932633399963    ) < 1e-3








'''simple docstring'''


def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b == 0:
								return 1
				if (b % 2) == 0:
								return actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)
				else:
								return a * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	) * actual_power(SCREAMING_SNAKE_CASE__				, int(b / 2	)	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if b < 0:
								return 1 / actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)
				return actual_power(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				print(power(-2, -3))




